Dataset Information

A Meta-Analysis of eHealth Interventions on Ischaemic Heart Disease Health Outcomes.

ABSTRACT:

Background

Electronic Health (eHealth) interventions as a secondary prevention tool to empower patients' health in decision-making and behaviour.

Objective

With the growing body of evidence supporting the use of eHealth interventions, the intention is to conduct a meta-analysis on various health outcomes of eHealth interventions among ischaemic heart disease (IHD) patients.

Methods

Based on PRISMA guidelines, eligible studies were searched through databases of Web of Science, Scopus, PubMed, EBSCOHost, and SAGE (PROSPERO registration CRD42021290091). Inclusion criteria were English language and randomised controlled trials published between 2011 to 2021 exploring health outcomes that empower IHD patients with eHealth interventions. RevMan 5.4 was utilised for meta-analysis, sensitivity analysis, and risk of bias (RoB) assessment while GRADE software for generating findings of physical health outcomes. Non-physical health outcomes were analysed using SWiM (synthesis without meta-analysis) method.

Results

This review included 10 studies, whereby, six studies with 895 participants' data were pooled for physical health outcomes. Overall, the RoB varied significantly across domains, with the majority was low risks, a substantial proportion of high risks and a sizeable proportion of unclear. With GRADE evidence of moderate to high quality, eHealth interventions improved low density lipoprotien (LDL) levels in IHD patients when compared to usual care after 12 months of interventions (SMD -0.26, 95% CI [-0.45, -0.06], I² = 0%, p = 0.01). Significance appraisal in each domain of the non-physical health outcomes found significant findings for medication adherence, physical activity and dietary behaviour, while half of the non-significant findings were found for other behavioural outcomes, psychological and quality of life.

Conclusions

Electronic Health interventions are found effective at lowering LDL cholesterol in long-term but benefits remain inconclusive for other physical and non-physical health outcomes for IHD patients. Integrating sustainable patient empowerment strategies with the advancement of eHealth interventions by utilising appropriate frameworks is recommended for future research.

SUBMITTER: Megat Kamaruddin PSN

PROVIDER: S-EPMC10022534 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

A Meta-Analysis of eHealth Interventions on Ischaemic Heart Disease Health Outcomes.

Megat Kamaruddin Puteri Sofia Nadira PSN Mohammed Nawi Azmawati A Abdul Manaf Mohd Rizal MR Yaman Mohamad Nurman MN Abd Malek Abdul Muizz AM

Global heart 20230316 1

<h4>Background</h4>Electronic Health (eHealth) interventions as a secondary prevention tool to empower patients' health in decision-making and behaviour.<h4>Objective</h4>With the growing body of evidence supporting the use of eHealth interventions, the intention is to conduct a meta-analysis on various health outcomes of eHealth interventions among ischaemic heart disease (IHD) patients.<h4>Methods</h4>Based on PRISMA guidelines, eligible studies were searched through databases of Web of Scienc ...[more]

PMID: 36936248

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Project description:BackgroundHeart failure (HF) is a common clinical syndrome associated with substantial morbidity, a heavy economic burden, and high risk of readmission. eHealth self-management interventions may be an effective way to improve HF clinical outcomes.ObjectiveThe aim of this study was to systematically review the evidence for the effectiveness of eHealth self-management in patients with HF.MethodsThis study included only randomized controlled trials (RCTs) that compared the effects of eHealth interventions with usual care in adult patients with HF using searches of the EMBASE, PubMed, CENTRAL (Cochrane Central Register of Controlled Trials), and CINAHL databases from January 1, 2011, to July 12, 2022. The Cochrane Risk of Bias tool (RoB 2) was used to assess the risk of bias for each study. The Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) criteria were used to rate the certainty of the evidence for each outcome of interest. Meta-analyses were performed using Review Manager (RevMan v.5.4) and R (v.4.1.0 x64) software.ResultsIn total, 24 RCTs with 9634 participants met the inclusion criteria. Compared with the usual-care group, eHealth self-management interventions could significantly reduce all-cause mortality (odds ratio [OR] 0.83, 95% CI 0.71-0.98, P=.03; GRADE: low quality) and cardiovascular mortality (OR 0.74, 95% CI 0.59-0.92, P=.008; GRADE: moderate quality), as well as all-cause readmissions (OR 0.82, 95% CI 0.73-0.93, P=.002; GRADE: low quality) and HF-related readmissions (OR 0.77, 95% CI 0.66-0.90, P<.001; GRADE: moderate quality). The meta-analyses also showed that eHealth interventions could increase patients' knowledge of HF and improve their quality of life, but there were no statistically significant effects. However, eHealth interventions could significantly increase medication adherence (OR 1.82, 95% CI 1.42-2.34, P<.001; GRADE: low quality) and improve self-care behaviors (standardized mean difference -1.34, 95% CI -2.46 to -0.22, P=.02; GRADE: very low quality). A subgroup analysis of primary outcomes regarding the enrolled population setting found that eHealth interventions were more effective in patients with HF after discharge compared with those in the ambulatory clinic setting.ConclusionseHealth self-management interventions could benefit the health of patients with HF in various ways. However, the clinical effects of eHealth interventions in patients with HF are affected by multiple aspects, and more high-quality studies are needed to demonstrate effectiveness.

Project description:ImportanceMobile health interventions are increasingly popular in pediatrics; however, it is unclear how effective these interventions are in changing health outcomes.ObjectiveTo determine the effectiveness of mobile health interventions for improving health outcomes in youth 18 years or younger.Data sourcesStudies published through November 30, 2016, were collected through PubMed, Cumulative Index to Nursing and Allied Health Literature, Educational Resources Information Center, and PsychINFO. Backward and forward literature searches were conducted on articles meeting study inclusion criteria. Search terms included telemedicine, eHealth, mobile health, mHealth, app, and mobile application.Study selectionSearch results were limited to infants, children, adolescents, or young adults when possible. Studies were included if quantitative methods were used to evaluate an application of mobile intervention technology in a primary or secondary capacity to promote or modify health behavior in youth 18 years or younger. Studies were excluded if the article was an unpublished dissertation or thesis, the mean age of participants was older than 18 years, the study did not assess a health behavior and disease outcome, or the article did not include sufficient statistics. Inclusion and exclusion criteria were applied by 2 independent coders with 20% overlap. Of 9773 unique articles, 36 articles (containing 37 unique studies with a total of 29 822 participants) met the inclusion criteria.Data extraction and synthesisOf 9773 unique articles, 36 articles (containing 37 unique studies) with a total of 29 822 participants met the inclusion criteria. Effect sizes were calculated from statistical tests that could be converted to standardized mean differences. All aggregate effect sizes and moderator variables were tested using random-effects models.Main outcomes and measuresChange in health behavior or disease control.ResultsA total of 29 822 participants were included in the studies. In studies that reported sex, the total number of females was 11 226 (53.2%). Of those reporting age, the average was 11.35 years. The random effects aggregate effect size of mobile health interventions was significant (n = 37; Cohen d = 0.22; 95% CI, 0.14-0.29). The random effects model indicated that providing mobile health intervention to a caregiver increased the strength of the intervention effect. Studies that involved caregivers in the intervention produced effect sizes (n = 16; Cohen d = 0.28; 95% CI, 0.18-0.39) larger than those that did not include caregivers (n = 21; Cohen d = 0.13; 95% CI, 0.02-0.25). Other coded variables did not moderate study effect size.Conclusions and relevanceMobile health interventions appear to be a viable health behavior change intervention modality for youth. Given the ubiquity of mobile phones, mobile health interventions offer promise in improving public health.

Project description:BackgroundMany organisations promote eHealth applications as a feasible, low-cost method of addressing mental ill-health and stress amongst their employees. However, there are good reasons why the efficacy identified in clinical or other samples may not generalize to employees, and many Apps are being developed specifically for this group. The aim of this paper is to conduct the first comprehensive systematic review and meta-analysis evaluating the evidence for the effectiveness and examine the relative efficacy of different types of eHealth interventions for employees.MethodsSystematic searches were conducted for relevant articles published from 1975 until November 17, 2016, of trials of eHealth mental health interventions (App or web-based) focused on the mental health of employees. The quality and bias of all identified studies was assessed. We extracted means and standard deviations from published reports, comparing the difference in effect sizes (Hedge's g) in standardized mental health outcomes. We meta-analysed these using a random effects model, stratified by length of follow up, intervention type, and whether the intervention was universal (unselected) or targeted to selected groups e.g. "stressed".Results23 controlled trials of eHealth interventions were identified which overall suggested a small positive effect at both post intervention (g = 0.24, 95% CI 0.13 to 0.35) and follow up (g = 0.23, 95% CI 0.03 to 0.42). There were differential short term effects seen between the intervention types whereby Mindfulness based interventions (g = 0.60, 95% CI 0.34 to 0.85, n = 6) showed larger effects than the Cognitive Behaviour Therapy (CBT) based (g = 0.15, 95% CI 0.02 to 0.29, n = 11) and Stress Management based (g = 0.17, 95%CI -0.01 to 0.34, n = 6) interventions. The Stress Management interventions however differed by whether delivered to universal or targeted groups with a moderately large effect size at both post-intervention (g = 0.64, 95% CI 0.54 to 0.85) and follow-up (g = 0.69, 95% CI 0.06 to 1.33) in targeted groups, but no effect in unselected groups.InterpretationThere is reasonable evidence that eHealth interventions delivered to employees may reduce mental health and stress symptoms post intervention and still have a benefit, although reduced at follow-up. Despite the enthusiasm in the corporate world for such approaches, employers and other organisations should be aware not all such interventions are equal, many lack evidence, and achieving the best outcomes depends upon providing the right type of intervention to the correct population.

Project description:BackgroundMental health conditions are considered the leading cause of disability, sickness absence, and long-term work incapacity. eHealth interventions provide employees with access to psychological assistance. There has been widespread implementation and provision of eHealth interventions in the workplace as an inexpensive and anonymous way of addressing common mental disorders.ObjectiveThis updated review aimed to synthesize the literature on the efficacy of eHealth interventions for anxiety, depression, and stress outcomes in employee samples in organizational settings and evaluate whether their effectiveness has improved over time.MethodsSystematic searches of relevant articles published from 2004 to July 2020 of eHealth intervention trials (app- or web-based) focusing on the mental health of employees were conducted. The quality and bias of all studies were assessed. We extracted means and SDs from publications by comparing the differences in effect sizes (Hedge g) in standardized mental health outcomes. We meta-analyzed these data using a random-effects model.ResultsWe identified a tripling of the body of evidence, with 75 trials available for meta-analysis from a combined sample of 14,747 articles. eHealth interventions showed small positive effects for anxiety (Hedges g=0.26, 95% CI 0.13-0.39; P<.001), depression (Hedges g=0.26, 95% CI 0.19-0.34; P<.001), and stress (Hedges g=0.25, 95% CI 0.17-0.34; P<.001) in employees' after intervention, with similar effects seen at the medium-term follow-up. However, there was evidence of no increase in the effectiveness of these interventions over the past decade.ConclusionsThis review and meta-analysis confirmed that eHealth interventions have a small positive impact on reducing mental health symptoms in employees. Disappointingly, we found no evidence that, despite the advances in technology and the enormous resources in time, research, and finance devoted to this area for over a decade, better interventions are being produced. Hopefully, these small effect sizes do not represent optimum outcomes in organizational settings.Trial registrationPROSPERO CRD42020185859; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=185859.

Project description:BackgroundRapid advancements in eHealth and mobile health (mHealth) technologies have driven researchers to design and evaluate numerous technology-based interventions to promote smoking cessation. The evolving nature of cessation interventions emphasizes a strong need for knowledge synthesis.ObjectiveThis systematic review and meta-analysis aimed to summarize recent evidence from randomized controlled trials regarding the effectiveness of eHealth-based smoking cessation interventions in promoting abstinence and assess nonabstinence outcome indicators, such as cigarette consumption and user satisfaction, via narrative synthesis.MethodsWe searched for studies published in English between 2017 and June 30, 2022, in 4 databases: PubMed (including MEDLINE), PsycINFO, Embase, and Cochrane Library. Two independent reviewers performed study screening, data extraction, and quality assessment based on the GRADE (Grading of Recommendations, Assessment, Development, and Evaluations) framework. We pooled comparable studies based on the population, follow-up time, intervention, and control characteristics. Two researchers performed an independent meta-analysis on smoking abstinence using the Sidik-Jonkman random-effects model and log risk ratio (RR) as the effect measurement. For studies not included in the meta-analysis, the outcomes were narratively synthesized.ResultsA total of 464 studies were identified through an initial database search after removing duplicates. Following screening and full-text assessments, we deemed 39 studies (n=37,341 participants) eligible for this review. Of these, 28 studies were shortlisted for meta-analysis. According to the meta-analysis, SMS or app text messaging can significantly increase both short-term (3 months) abstinence (log RR=0.50, 95% CI 0.25-0.75; I2=0.72%) and long-term (6 months) abstinence (log RR=0.77, 95% CI 0.49-1.04; I2=8.65%), relative to minimal cessation support. The frequency of texting did not significantly influence treatment outcomes. mHealth apps may significantly increase abstinence in the short term (log RR=0.76, 95% CI 0.09-1.42; I2=88.02%) but not in the long term (log RR=0.15, 95% CI -0.18 to 0.48; I2=80.06%), in contrast to less intensive cessation support. In addition, personalized or interactive interventions showed a moderate increase in cessation for both the short term (log RR=0.62, 95% CI 0.30-0.94; I2=66.50%) and long term (log RR=0.28, 95% CI 0.04-0.53; I2=73.42%). In contrast, studies without any personalized or interactive features had no significant impact. Finally, the treatment effect was similar between trials that used biochemically verified or self-reported abstinence. Among studies reporting outcomes besides abstinence (n=20), a total of 11 studies reported significantly improved nonabstinence outcomes in cigarette consumption (3/14, 21%) or user satisfaction (8/19, 42%).ConclusionsOur review of 39 randomized controlled trials found that recent eHealth interventions might promote smoking cessation, with mHealth being the dominant approach. Despite their success, the effectiveness of such interventions may diminish with time. The design of more personalized interventions could potentially benefit future studies.Trial registrationPROSPERO CRD42022347104; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=347104.

Project description:ImportanceProsocial interventions encourage voluntary actions that benefit others. Community solidarity in response to the COVID-19 pandemic, expanding mutual aid programs, and health workforce issues have accelerated prosocial health interventions.ObjectiveTo investigate the association of prosocial interventions with health outcomes in clinical trials and observational studies.Data sourcesIn this systematic review and meta-analysis informed by the Cochrane Handbook for Systematic Reviews of Interventions, 5 databases (MEDLINE [via PubMed], Embase, CINAHL, PsycInfo, and Scopus) were searched from database inception through February 23, 2023. The search included terms for altruism and prosocial behaviors, health outcomes, and study type.Study selectionIncluded studies, determined by multiple reviewers, compared health outcomes in a prosocial intervention group with a nonintervention group.Data extraction and synthesisFollowing the Preferred Reporting Items for Systematic Reviews and Meta-analyses guideline, data extraction and synthesis captured quantitative and qualitative data. To pool data from quantitative studies, random-effects meta-analyses were used to estimate the impact of prosocial interventions. To combine data from quantitative and qualitive studies, data were transformed into qualitative narratives using meta-aggregation.Main outcomes and measuresThe main outcome was whether prosocial interventions were associated with improved health outcomes. Barriers to and facilitators of implementation of these interventions were assessed.ResultsThe search identified 5229 citations; 30 studies were included in the synthesis. Studies indicated that prosocial interventions were associated with positive health outcomes for givers (17 studies [56.7]) and recipients (8 [26.7%]). Prosocial interventions included acts of kindness (12 studies [40.0%]), cash gifts (7 [23.3%]), pay-it-forward approaches (6 [20.0%]), and expressions of kindness (5 [16.7%]). Improvements were reported in depression, testing for sexually transmitted diseases, vaccine uptake, physical activity, and individual biomarkers. Data from 6 studies (20.0%) demonstrated that pay-it-forward approaches were associated with increased uptake of diagnostic tests or vaccines among vulnerable groups (moderate certainty of evidence). Data from 14 studies (46.7%) suggested that community connectedness facilitated prosocial interventions. Shared vulnerabilities among groups (eg, sexual minority individuals, older adults) may provide a context for collective mobilization to improve health in local communities.Conclusions and relevanceThis systematic review and meta-analysis found that prosocial interventions were associated with improved health outcomes among vulnerable groups and have been useful for addressing health disparities. Further research is needed to develop and evaluate prosocial interventions.

Dataset Information

A Meta-Analysis of eHealth Interventions on Ischaemic Heart Disease Health Outcomes.

Background

Objective

Methods

Results

Conclusions

Publications

A Meta-Analysis of eHealth Interventions on Ischaemic Heart Disease Health Outcomes.

Similar Datasets

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